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刘响,助理教授

联系信息

姓名:刘响 办公电话: +86-10-627-94823 邮箱:发送邮件 传真号码:+86-10-6279-4399 地点:清华大学舜德楼中514 教师主页:

个人简介

刘响,清华大学工业工程系助理教授。2019年博士毕业于美国密西根大学。研究方向为医疗服务系统工程;包括疾病建模预测、临床决策优化,医院运营管理,公共卫生医疗政策。研究方法包括随机过程控制、动态规划、优化、数据挖掘和分析。
研究获2020年北京数智医保创新竞赛潜力奖,2019年生产与运营管理学会(POMS)Wickham Skinner最佳论文奖亚军,2018年管理科学与筹学会(INFORMS)制造与服务运营管理分会(MSOM)数据驱动研究挑战决赛入围(Data Driven Research Challenge Finalist), 2013年管理科学与筹学会(INFORMS)公共部门运筹学分会(PSOR)最佳论文荣誉奖(PSOR Best Paper Award Honorable Mention)。

刘响老师每年都有计划招收博士和硕士, 欢迎具有良好数学基础,擅长编程,并且热爱学术的同学发送简历到:xiang-liu@mail.tsinghua.edu.cn

所获奖励

北京数智医保创新竞赛潜力奖,2020
生产与运营管理学会(POMS)Wickham Skinner最佳论文奖亚军,2019
管理科学与筹学会(INFORMS)制造与服务运营管理分会(MSOM)数据驱动研究挑战决赛入围(Data Driven Research Challenge Finalist), 2018
美国泌尿科学会(American Urological Association)年会最佳海报奖,2017
管理科学与筹学会(INFORMS)公共部门运筹学分会(PSOR)最佳论文奖入围(PSOR Best Paper Award Finalist),2013

教育背景

美国密西根大学,工业工程,博士,2019
美国加州大学伯克利分校,工业工程与运筹学,硕士,2014
美国密西根大学,工业工程,学士(以最高荣誉毕业),2013
上海交通大学,机械工程,学士,2013

工作经历

2019年10月至今,清华大学,工业工程系,助理教授

讲授课程

中国产业研究,清华大学
战略管理,清华大学
动态规划导论,清华大学
IOE 202 Operations Modeling,密西根大学

研究兴趣

应用:疾病建模预测、临床决策优化,医院运营管理,公共卫生医疗政策
方法:随机过程控制,动态规划、优化、数据挖掘和分析

论文发表

工作论文

1. X. Liu, M. S. Lavieri, J. E. Helm, T. A. Skolarus, “Time for accountability: Are readmission responsibility windows too long?” Major Revision at Operations Research, 2021.

2. M. Li, S. Nassiri, X. Liu, C. Elimoottil, “How Does Telemedicine Shape Physician's Practice in Mental Health?” Submitted to Management Science, 2021.

3. N. Li, F. Yuan, X. Ma, X. Liu, “Allocation of Telemedicine Capacity in a Regional Hierarchical System for Community-Based Care,” Submitted to IISE Transactions on Healthcare Systems Engineering, 2020.


期刊

1. X. Liu, et al. "Comparison of Telemedicine Versus In-Person Visits on Impact of Downstream Utilization of Care," Telemedicine and e-Health, https://doi.org/10.1089/tmj.2020.0286, 2021.

2. P. Kirk, X. Liu, et al., "Dynamic readmission prediction using routine postoperative laboratory results after radical cystectomy," Urologic Oncology: Seminars and Original Investigations, vol. 38, no. 4, pp. 255–261, 2020.

3. A. Lee, X. Liu, et al., “Role of post-acute care on hospital readmission after high-risk surgery,” Journal of Surgical Research, vol. 234, pp. 116–122, 2019.

4. P. Kirk, T. A. Skolarus, B. Jacobs, Y. Qin, B. Li, M. Sessine, X. Liu, et al., “Characterizing ‘Bounce-back’ Readmissions After Radical Cystectomy,” BJU International, vol. 12 pp. 1416–1423, 2019.

5. G.-G. Garcia, K. Nitta, M. S. Lavieri, C. Andrews, X. Liu, et al., “Using Kalman Filtering to Forecast Disease Trajectory for Patients with Normal Tension Glaucoma,” American Journal of Ophthalmology, vol. 60, no. 9, pp. 2857–2857, 2019.

6. X. Liu, M. Hu, J. E. Helm, M. S. Lavieri, and T. A. Skolarus, “Missed opportunities in preventing hospital readmissions: Redesigning post-discharge checkup policies,” Production and Operations Management, vol. 27, no. 12, pp. 2226–2250, 2018.

7. W. Hu, M. S. Lavieri, A. Toriello, and X. Liu, “Strategic health workforce planning,” IIE Transactions, vol. 48, no. 12, pp. 1127–1138, 2016.

8. N. Krishnan, X. Liu, et al., “A model to optimize followup care and reduce hospital readmissions after radical cystectomy,” The Journal of Urology, vol. 195, no. 5, pp. 1362– 1367, 2016.

9. G. J. Schell, M. S. Lavieri, J. E. Helm, X. Liu, et al., “Using filtered forecasting techniques to determine personalized monitoring schedules for patients with open-angle glaucoma,” Ophthalmology, vol. 121, no. 8, pp. 1539–1546, 2014.


会议

1. G.-G. Garcia, M. S. Lavieri, C. Andrews, X. Liu, et al., “Using a Machine Learning Technique Called Kalman Filtering to Forecast Conversion from Ocular Hypertension to Primary Open Angle Glaucoma,” Investigative Ophthalmology & Visual Science, vol. 50, no. 9 pp. 2857–2857, 2019.

2. M. Sessine, T. Borza, A. Weizer, P. Kirk, X. Liu, et al., “MP71-02 reframing readmission reduction incentives after radical cystectomy,” The Journal of Urology, vol. 199, no. 4, e943, 2018.

3. P. Kirk, X. Liu, et al., “MP71-04 assessing laboratory parameters and readmissions after radical cystectomy,” The Journal of Urology, vol. 199, no. 4, e944, 2018.

4. S. Finley, S. Joshi, T. Borza, X. Liu, et al., “MP04-06 personalized decision support tool to prevent hospital readmission for patients treated with radical cystectomy,” The Journal of Urology, vol. 197, no. 4, e30, 2017.

5. M. S. Lavieri, X. Liu, et al., “Using kalman filtering to personalize the monitoring of persons with normal tension glaucoma,” Investigative Ophthalmology & Visual Science, vol. 58, no. 8, pp. 2870–2870, 2017.

6. N. Krishnan, X. Liu, et al., “PD25-08 a model to optimize follow-up care and reduce hospital readmissions after radical cystectomy,” The Journal of Urology, vol. 193, no. 4, e563, 2015.


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